Summary of the Copper Deposits of Americas

Using data compiled by Geoscience Australia, the Geological Survey of Canada and the United States Geological Survey

Author

Yuri Gomes

Published

February 24, 2023

1 On Dataset

This report brings a summary of the Americas’s Copper deposits contained in the Critical Minerals Mapping Initiative (CMMI) database CMMI is a collection of geochemical analysis of 7,000 samples from mineral deposits in 60 countries.

This report is for education and science communication only. You are free to share, copy and redistribute the material in any medium or format. You may not use the material for commercial purposes.

The full dataset as well as its information, authors and metadata can be accessed here.

Show the code

knitr::include_graphics("images/chuquicamata_cu_mine.jpg")

Fonte: Unplash.com

2 Data Import

Geoscience Australia, the Geological Survey of Canada, and the US Geological Survey compiled CMMI from publicly available sources, including mineral deposit, geochemistry, and the OSNACA databases.

The original dataset is divided into 340 columns showing Sample ID, Analytes, Limit of Detections, Countries, Coordinates, among other information, and 9513 rows.

3 Data Tidying and Data Filtering

For this analysis, we have one target element: Copper (Cu), and we’ll explore its distribution along the countries’ deposits.

So we need to filter the dataset prior to leave only the necessary information.

Show the code

copper <- tibble(cmmi_dataset$primary_commodities)

cmmi_americas <- cmmi_dataset |>
  dplyr::filter(country %in% c("MEX", "USA", "CAN", "BRA", "ARG", "CHL", "PER", "URY", "ECU", "COL")) |>
  dplyr::select(sample_name, primary_commodities, deposit_name, deposit_environment, country, 
                state, cu_ppm, cu_detection_limit, cu_method, 
                deposit_longitude_wgs84,deposit_latitude_wgs84)|>
  drop_na(cu_ppm)|>
  dplyr::filter(cu_ppm >= 50) |>
  mutate(country = countrycode(country, "iso3c", "country.name"))

4 Deposits’ Location

You may locate all the 712 deposits in the map below:

5 Copper grade distribution per country

Grade is one of the most important parameters to determine the value and the importance of a deposit.

In the boxplot below, you can see the copper grade distribution per country.

Using country as id variables

6 References

Champion, D., Raymond, O., Huston, D., VanDerWielen, S., Sexton, M., Bastrakov, E., Schroder, I., Butcher, G., Hawkins, S., Lane, J., McAlpine, S., Czarnota, K., Britt, A., Granitto, M., Hofstra, A., Kreiner, D., Emsbo, P., Kelley, K., Wang, B., Case, G., Graham, G., Lauzière, K., Lawley, C., Gadd, M., Pilote, J-L., Létourneau, F., Lisitsin, V., Haji Egeh, A. 2021. Critical Minerals in Ores - geochemistry database. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/145496

7 Credits

Created by Yuri Gomes for the Reproducible Reports in R Course, from the Summer Courses of the Institute of Mathematics and Statistics of the University of São Paulo (USP).

--- title: "Summary of the Copper Deposits of Americas" subtitle: "Using data compiled by Geoscience Australia, the Geological Survey of Canada and the United States Geological Survey" author: "Yuri Gomes" date: "`r Sys.Date()`" toc: true number-sections: true format: html: theme: light: flatly dark: darkly code-tools: true self-contained: true code-fold: true code-summary: "Show the code" editor: visual --- ## On Dataset This report brings a summary of the Americas's Copper deposits contained in the Critical Minerals Mapping Initiative (CMMI) database CMMI is a collection of geochemical analysis of 7,000 samples from mineral deposits in 60 countries. This report is for education and science communication only. You are free to share, copy and redistribute the material in any medium or format. You may not use the material for commercial purposes. The full dataset as well as its information, authors and metadata can be accessed [here](https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/145496). ```{r images, fig.align= "left", fig.alt = "Chuquicamata Copper Mine, Chile, 2019"} knitr::include_graphics("images/chuquicamata_cu_mine.jpg") ``` ::: {style="text-align: right"} Fonte: <a href="https://unsplash.com/pt-br/fotografias/sRuz_SXLjCI">Unplash.com</a> ::: <div style="text-align: justify"> ## Data Import Geoscience Australia, the Geological Survey of Canada, and the US Geological Survey compiled CMMI from publicly available sources, including mineral deposit, geochemistry, and the OSNACA databases. ```{r, include = FALSE, echo = FALSE} library(tidyverse) library(ggplot2) library(leaflet) library(here) library(countrycode) library(reshape2) library(scales) ``` ```{r, include= FALSE, echo= FALSE} cmmi_dataset <- readr::read_csv( here::here("projeto_report_geoquimica/CriticalMineralDepositsGeochemistry.csv")) |> janitor::clean_names() ``` The original dataset is divided into `r ncol(cmmi_dataset)` columns showing Sample ID, Analytes, Limit of Detections, Countries, Coordinates, among other information, and `r nrow(cmmi_dataset)` rows. ## Data Tidying and Data Filtering For this analysis, we have one target element: Copper (Cu), and we'll explore its distribution along the countries' deposits. So we need to filter the dataset prior to leave only the necessary information. ```{r} copper <- tibble(cmmi_dataset$primary_commodities) cmmi_americas <- cmmi_dataset |> dplyr::filter(country %in% c("MEX", "USA", "CAN", "BRA", "ARG", "CHL", "PER", "URY", "ECU", "COL")) |> dplyr::select(sample_name, primary_commodities, deposit_name, deposit_environment, country, state, cu_ppm, cu_detection_limit, cu_method, deposit_longitude_wgs84,deposit_latitude_wgs84)|> drop_na(cu_ppm)|> dplyr::filter(cu_ppm >= 50) |> mutate(country = countrycode(country, "iso3c", "country.name")) ``` ## Deposits' Location You may locate all the `r length(unique(cmmi_americas$deposit_name))` deposits in the map below: ```{r echo = FALSE} ### Defining coordinates leaflet::leaflet(data = cmmi_americas) |> leaflet::addCircleMarkers(lng = ~deposit_longitude_wgs84, lat = ~deposit_latitude_wgs84, radius = 5, popup = paste("Name: ", cmmi_americas$deposit_name, " ", "Copper (ppm): ", cmmi_americas$cu_ppm, " ", "Latitude: ", cmmi_americas$deposit_latitude_wgs84, " ", "Longitude: ", cmmi_americas$deposit_longitude_wgs84, " ", "Environment: ", cmmi_americas$deposit_environment, " ", "Country: ", cmmi_americas$country)) |> leaflet::addProviderTiles(provider = "Esri.WorldImagery") ``` ## Copper grade distribution per country Grade is one of the most important parameters to determine the value and the importance of a deposit. In the boxplot below, you can see the copper grade distribution per country. ```{r echo=FALSE} boxplot_df <- cmmi_americas |> select(country, cu_ppm) melted_boxplot_df <- melt(boxplot_df) ggplot(melted_boxplot_df, aes(x = country, y = value, fill = country)) + geom_boxplot(color = "black", outlier.shape = NA) + scale_fill_brewer()+ theme(axis.text.x=element_text(angle=0,hjust=1, face="bold", color = "black"), axis.text.y=element_text(face="bold", color = "black"))+ theme_minimal() + labs(title="Copper Grade Distribution per Country", x = "", y = "Copper (in ppm)")+ stat_boxplot(geom='errorbar') + scale_y_continuous(trans='log10', labels = comma) ``` ## References Champion, D., Raymond, O., Huston, D., VanDerWielen, S., Sexton, M., Bastrakov, E., Schroder, I., Butcher, G., Hawkins, S., Lane, J., McAlpine, S., Czarnota, K., Britt, A., Granitto, M., Hofstra, A., Kreiner, D., Emsbo, P., Kelley, K., Wang, B., Case, G., Graham, G., Lauzière, K., Lawley, C., Gadd, M., Pilote, J-L., Létourneau, F., Lisitsin, V., Haji Egeh, A. 2021. Critical Minerals in Ores - geochemistry database. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/145496 ## Credits Created by *Yuri Gomes* for the Reproducible Reports in R Course, from the Summer Courses of the Institute of Mathematics and Statistics of the University of São Paulo (USP).